How to safeguard AI at every touchpoint, without slowing innovation

Embracing the Era of Ubiquitous AI in the Workplace

Today, one in three employees in Britain uses artificial intelligence (AI) as a part of their daily work routine. The AI development and uptake in the IT and telecom industries is even higher, reaching 66% of the polled workforce.

AI has transitioned from a futuristic concept to an essential resource that enables workers to enhance productivity and innovation.

According to McKinsey, employees are more eager to gain AI skills than many leaders realise. They explore AI capabilities, using tools like ChatGPT to search for information, brainstorming ideas and debugging code. They are not just using AI. They are embracing it, genuinely seeking ways to streamline processes and deliver better results.

Many businesses tend to respond to this enthusiasm with caution, often opting for restrictions instead of supporting it. But banning the use of generative AI tools within the workplace isn’t the solution.

Prohibition of AI development can hamper innovation and breed frustration. Users may resort to unapproved workarounds (i.e., shadow IT), which could compromise the very security and compliance frameworks you are aiming to protect.

To address this issue, rather than saying no to AI development, you should shift your focus from prohibition to safe empowerment. You should figure out how to foster an environment where employees feel supported in exploring and leveraging AI’s potential, without fear of unintentionally causing a cyber security incident or violating compliance regulations.

In this article, we will explore how to secure your AI development across all touchpoints and create a proactive work environment where employees can flourish alongside advancing technologies, and drive growth for your business as a whole.

The CIO Challenge: Balancing Speed and Security in the Age of AI

Innovation is critical for business success. Nevertheless, generative AI (GenAI) applications introduce unprecedented new complexities.

Thus, technology leaders are understandably concerned about how to balance innovation with the essential need to protect business assets from data leakage and compliance violations, especially when integrating third-party advanced AI models into their workflows.

Why Traditional Security Models Are Falling Short

Traditional security measures were designed for static perimeters and predictable systems with precise intent and behaviour. The data resided within clearly defined boundaries, with the assumption that the same input produced the same output.

However, AI systems don’t behave like traditional software. They dynamically respond to inputs, making them susceptible to manipulation via natural language. A single prompt can trigger unpredictable chains of actions, from sharing sensitive data with third parties to generating unsafe outputs.

For instance, employees using AI tools for data analysis might inadvertently leak customer information or violate industry regulations by feeding sensitive data into an external AI system.

That makes traditional security models inadequate for AI-specific threats such as prompt injection, model poisoning, data leaks and unauthorised agent-to-agent communications.

Finding the Balance: Guardrails Over Barriers

Rather than imposing strict barriers that could slow productivity, technology leaders and Chief Information Officers (CIOs) should implement flexible “guardrails” on AI systems. These dynamic, context-aware controls assess inputs, model behaviour and outputs in real time.

They help ensure compliance with your business’s policies without blocking or slowing down legitimate innovation and processes.

For instance, you could set up a vetting process for AI applications before deployment. This way, your employees will be able to use checked and authorised AI tools for data analysis, significantly accelerating decision-making. At the same time, you will maintain compliance by only allowing applications that have passed the security assessment.

Speed Meets Security: A Cohesive Strategy for Success

CIOs don’t have to choose between speed and security. They have to craft a robust strategy that harmonises both so that businesses can thrive in an environment where innovation and security coexist seamlessly.

Navigating the Complexities of Shadow AI

59% of senior cyber security leaders interviewed by the Purple Book Community in 2026 have recognised that their employees use unmanaged applications and tools without the formal oversight of IT departments.

Nonetheless, the majority of shadow AI is not malicious; it’s practical. Users and developers typically embrace AI-based tools with the best intentions of enhancing efficiency and productivity, but without fully understanding the implications for data security.

Consequently, they might unwillingly create blind spots around data security and compliance, putting vital corporate information at risk.

Understanding the Risk of Shadow AI

Consider employees using third-party generative AI models and copilots to expedite routine tasks. These tools rely on prompts. The richer the prompt, the better the output. However, without proper controls in place, a richer prompt also means a greater risk.

Therefore, if a user enters more detailed information into their prompt to achieve better results, they may accidentally include private data. Without realising it, the user has just created an exposure because they have no visibility of what happens in the background.

Example #1. Data analysis. When your employees share sensitive customer data with an unsecured AI analytics platform, they unknowingly put your business at risk of a data leak. Violating regulations such as the General Data Protection Regulation (GDPR) or the Health Insurance Portability and Accountability Act (HIPAA) can lead to hefty fines and damage your reputation.

Example #2. Software development. Developers are often under pressure to deliver projects on time. AI code assistant tools such as GitHub Copilot and Claude Code help them ship software faster by assigning tasks to autonomous agents. Yet, in their haste, they might also unintentionally expose secrets, proprietary code, or personally identifiable information (PII).

Shedding Light on Shadow AI

The real danger is not your employees. It is the lack of visibility of tools, platforms and solutions available within your business.

Therefore, to really eliminate shadow AI, you must proactively address the root cause. Look for blind spots. It will enable you to achieve real-time visibility and control over all your workflows, ensuring that security and compliance remain top priorities.

This way you will reduce the risk of shadow AI and foster an environment where innovation can thrive within secure boundaries.

From “No” to “Know”: Gain Real-Time Visibility Across All Workflows

You can’t protect what you can’t see. To address this visibility issue and shield your business from the threats associated with rapid AI development, don’t halt its adoption; shape it instead.

Acquire visibility into how AI tools are used across your businesses, not just which platforms are approved, monitored, audited and regulated by policies. That includes understanding patterns of data sharing and usage, as well as where risk is most likely to emerge.

Start by prioritising real-time visibility across all your AI touchpoints, such as, for example:

• Public chat applications. Many employees are turning to public chat tools to enhance productivity. However, these platforms may lack robust security measures, putting sensitive information at risk. Ensure your IT department has visibility into what employees are sharing and how they are using the tools. Collect and aggregate data into a centralised platform. You will gain valuable insights into usage patterns and be able to set up alerts for potential anomalies.
• Developer workflows with coding copilots. Developers frequently use coding assistants or AI copilots to accelerate their coding processes. In fact, recent research from the Massachusetts Institute of Technology (MIT) Sloan School of Management indicates that developers using Copilot increased their coding activities by 12.4%. Nevertheless, while these tools improve efficiency, they may also inadvertently expose proprietary code and introduce dangerous vulnerabilities. Regularly audit and monitor these workflows. You will proactively safeguard intellectual property and shield your business from cyber attacks.
• Internally developed AI applications. If you build AI solutions in-house, ensure you have full visibility over data input practices and model outputs. It will help you preserve data integrity and compliance.

Ultimately, implementing comprehensive monitoring, inspection mechanisms and regular audits is the first step toward achieving safe and rapid scalability in AI adoption.

AI Requires Policies at the Point of Interaction, Not Roadblocks

Sensitive information exposure is the second most critical security issue listed in OWASP’s 2025 Top 10 for Large Language Model Applications. The most effective strategy to prevent this threat is by implementing protective measures directly at the point where users interact with data, tools and models.

Dynamic Policy Enforcement: Blocking Risks Before Execution

Dynamic policy enforcement helps you mitigate this risk by leveraging cutting-edge tools and models to automatically detect sensitive personal data or intellectual property during user interactions in real-time.
This proactive approach ensures that the enforcement of data protection policies is smoothly integrated into daily workflows, without disrupting your employee productivity. It includes:

• Real-time scanning. This solution ensures that user inputs are evaluated as they are crafted. For instance, as the user types a query, natural language processing (NLP) uses advanced machine learning algorithms to analyse the input for key terms or patterns that may indicate a potential exposure of confidential data.
• Granular control. Policies tailored to specific departments and user roles, allow you to maintain strict access controls without stifling innovation. Attribute-based access control (ABAC) and policy-based access control (PBAC) enable you to implement sophisticated authorisation models that adapt to each specific context.
• Sensitive data protection. Automatic anonymisation and data privacy enforcement can instantly redact queries and sanitise code to prevent data leaks and exfiltration of secrets. For example, when the system detects sensitive content, it intelligently redacts just those parts. That lets you prevent a data leak while allowing the rest of the query to proceed without interruption.

These technical controls and policies govern how users and systems interact with AI tools and models. They transform manual policy enforcement into a streamlined, automated process that operates in real time across your business’s ecosystem and strengthens your AI security posture.

3 Key Benefits of Point-of-Interaction Dynamic Policy Enforcement

Dynamic policy enforcement at points of interaction enhances the user experience while protecting sensitive data, creating a secure and efficient workplace culture. This approach serves as an invisible safety net, guiding employees towards safe behaviour and encouraging them to utilise AI tools confidently. Moreover, this strategy:

1. Guarantees a seamless user experience. Instead of imposing rigid limitations that may frustrate users, dynamic policy enforcement creates a subtle but powerful layer of protection, allowing employees to engage with AI systems confidently.
2. Offers frictionless security. Users are guided away from potential risks without feeling constantly monitored. When security isn’t an obstacle to your workforce’s productivity, it fosters a culture of safety and compliance.
3. Doesn’t disrupt workflows. If a user attempts to submit a query that contains a mix of sensitive and non-sensitive information, the system understands the context. It then filters the sensitive elements in the background, without interrupting the process.

Scaling Innovation Confidently with Acora

A secure AI development drives innovation and success. However, many businesses trying to unlock the full potential of AI encounter roadblocks due to skill gaps and a lack of real-world experience. That can lead to missed opportunities and costly mistakes.

At Acora, we truly understand these challenges. From exploration to execution and refinement, our commitment to helping you explore, create, run and refine means we are with you every step of the way on your digital transformation journey.

We leverage SentinelOne’s Prompt Security platform to empower businesses to confidently adopt and scale GenAI models, developer copilots and agentic workflows. With features like real-time inspection and dynamic policy enforcement, this strategic partnership helps you:

• Reduce the likelihood of data breaches and compliance violations caused by the unchecked use of AI tools.
• Secure your entire AI ecosystem without slowing down innovation and creativity.
• Empower your employees to use AI capabilities safely.

As you embrace the transformative power of AI, we will assist you in exploring its vast potential, driving efficiency and creativity while safeguarding your most valuable assets.

Get in touch with our team of experts. Your initiatives will not only align with your business goals but also flourish within a secure AI development framework that fosters innovation and growth.